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In order to give Mobile Context-aware Recommender Systems (MCRS) the capa-bility to provide the mobile user information matching his/her situation and adapted tothe evolution of his/her interests, our contribution consists of mixing bandit algorithm(BA) and case-based reasoning (CBR) methods in order to tackle these two issues: Finding situations that are similar to the current one (CBR); Making the deal between exploring the user interests and recommending the most relevant content according to the current situation (BA).The remainder of the paper is organized as follows. Section 2 reviews some relatedworks. Section 3 presents the proposed recommendation algorithm. The experimentalevaluation is described in Section 4. The last Section concludes the paper and pointsout possible directions for future work.2 BackgroundWe reference in the following recent relevant recommendation techniques that tacklethe both issues namely: following the evolution of user’s interests and managing theuser’s situation.2.1 Following the evolution of user’s interestsThe trend today on recommender systems is to suggest relevant information to users,using supervised machine learning techniques. In these approaches, the recommendersystem has to execute two steps: (1) The learning step, where the system learns fromsamples and gradually adjusts its parameters; (2) The exploitation step, where newsamples are presented to the system to perform a generalization [14]. These approaches suffer from difficulty in following the evolution of the user’s in-terests. Some works found in the literature [3, 11] address this problem as a need forbalancing exploration and exploitation studied in the “bandit algorithm”. A banditalgorithm B exploits its past experience to select documents that appear more fre-quently. Besides, these seemingly optimal documents may in fact be suboptimal, dueto imprecision in B’s knowledge. In order to avoid this undesired situation, B has toexplore documents by actually choosing seemingly suboptimal documents so as togather more information about them. Exploitation can decrease short-term user’s sat-isfaction since some suboptimal documents may be chosen. However, obtaining in-formation about the documents’ average rewards (i.e., exploration) can refine B’sestimate of the documents’ rewards and in turn increase long-term user’s satisfaction.Clearly, neither a purely exploring nor a purely exploiting algorithm works best ingeneral, and a good tradeoff is needed. The authors on [3, 11] describe a smart way tobalance exploration and exploitation in the field of recommender systems. However,none of them consider the user’s situation during the recommendation.

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2.2 Managing the user’s situationFew research works are dedicated to manage the user’s situation on recommendation.In [1, 4, 5] the authors propose a method which consists of building a dynamic user’sprofile based on time and user’s experience. The user’s preferences in the user’s pro-file are weighted according to the situation (time, location) and the user’s behavior.To model the evolution on the user’s preferences according to his temporal situationin different periods, (like workday or vacations), the weighted association for theconcepts in the user’s profile is established for every new experience of the user. Theuser’s activity combined with the users profile are used together to filter and recom-mend relevant content.. Another work [2] describes a MCRS operating on three dimensions of context thatcomplement each other to get highly targeted. First, the MCRS analyzes informationsuch as clients’ address books to estimate the level of social affinity among users.Second, it combines social affinity with the spatiotemporal dimensions and the user’shistory in order to improve the quality of the recommendations. Each work cited above tries to recommend interesting information to users on con-textual situation; however they do not consider the evolution of the user’s interest. To summarize, none of the mentioned works tackles both problems. This is pre-cisely what we intend to do with our approach, exploiting the following new features: Inspired by models of human reasoning developed by [7] in robotic, we propose to consider the users situation in the bandit algorithm by using the case-based reason- ing technique, which is not considered in [3, 4, 14]. In [3, 14] authors use a smart bandit algorithm to manage the explora- tion/exploitation strategy, however they do not take into account the content in the strategy. Our intuition is that, considering the content when managing the explora- tion/exploitation strategy will improve it. This is why we propose to use content- based filtering techniques together with ε-greedy algorithm.In what follows, we summarize the terminology and notations used in our contribu-tion, and then we detail our methods for inferring the recommendation.3 The proposed MCRS algorithm3.1 Terminology and NotationsUser Profile. The user profile is composed of the user’s personal data and other dy-namic information, including his preferences, his calendar and the history of his inter-actions with the system.User Preferences. Preferences are deduced during user navigation activities. Theycontain the set of navigated documents during a situation. A navigation activity ex-presses the following sequence of events: (i) the user logs in the system and navigates

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across documents to get the desired information; (ii) the user expresses his/her prefer-ences on the visited documents. We assume that a visited document is relevant, andthus belongs to the user’s preferences, if there are some observable user’s behaviorsthrough 2 types of preference: The direct preference: the user expresses his interest in the document by inserting a rate, like for example putting stars (“*”) at the top of the document. The indirect preference: it is the information that we extract from the user system interaction, for example the number of clicks or the time spent on the visited doc- uments.Let UP be the preferences submitted by a specific user to the system at a given situa-tion. Each document in UP is represented as a single vector d=(c 1,...,cn), where ci (i=1,.., n) is the value of a component characterizing the preferences of d. We consider thefollowing components: the total number of clicks on d, the total time spent reading d,the number of times d was recommended, and the direct preference rate on d.History. All the interactions between the user and the system are stored together withthe corresponding situations in order to exploit this data to improve the recommenda-tion process.Calendar. The user’s calendar has information concerning the user’s activities, likemeetings. Time and location information is automatically inferred by the system.User Situation. A situation S is represented as a triple whose features X are the valuesassigned to each dimension: S = (Xl, Xt, Xs), where Xl (resp. Xt and Xs) is the value ofthe location (resp. time and social) dimension. Suppose the user is associated to: the location "48.8925349, 2.2367939" from hisphone’s GPS; the time "Mon Oct 3 12:10:00 2011" from his phone’s watch; and themeeting with Paul Gerard from his calendar. To build the situation, we associate tothis kind of low level data, directly acquired from mobile devices capabilities, moreabstracted concepts using ontologies reasoning means. Location: We use a local spatial ontology to represent and reason on geographic information. Using this ontology, for the above example, we get, from location "48.8925349, 2.2367939", the value “Paris” to insert in the location dimension of the situation. Time: To allow a good representation of the temporal information and its manipu- lation, we propose to use OWL-Time ontology [6] which is today a reference for representing and reasoning about time. We propose to base our work on this ontol- ogy and extend it if necessary. Taking the example above, for the time value "Mon Oct 3 12:10:00 2011", we get, using the OWL-Time ontology, the value “work- day”. Social connection: The social connection refers to the information of the user’s interlocutors (e.g. a friend, an important customer, a colleague or his manager). We

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use the FOAF Ontology [9] to describe the social network by a set of concepts and properties. For example, the information about “the meeting with Paul Gerard” can yield the value “wine client” for the social dimension.3.2 The bandit algorithmIn our MCRS, documents’ recommendation is modeled as a multi-armed bandit prob-lem. Formally, a bandit algorithm proceeds in discrete rounds t = 1,…T. For eachround t, the algorithm performs the following tasks: Task 1. It observes the user’s situation St and d set Dt of documents with their fea- ture vectors xt,d for d  Dt. The vector xt,d corresponds to the information of both user’s situation St and document d. Task 2. Based on observed rewards in previous rounds, it chooses an document dt  Dt, and receives reward r whose expectation depends on both the user’s situa- t ,dt tion St and the document at. Task 3. It improves its document-selection strategy with the new observation.  T In tasks 1 to 3, the total T-round reward of D is defined as r while the op-  where d t 1 t , d t Ttimal expected T-round reward is defined as  r t * is the document t 1 t , d t *with maximum expected reward at round t. Our goal is to design the bandit algorithmso that the expected total reward is maximized. In the field of document recommendation, when a document is presented to the us-er and this one selects it by a click, a reward of 1 is incurred; otherwise, the reward is0. With this definition of reward, the expected reward of a document is precisely itsClick Through Rate (CTR). The CTR is the average number of clicks on a recom-mended document, computed diving the total number of clicks on it by the number oftimes it was recommended. It is important to know here that no reward rt,d is observedfor unchosen documents d ≠ dt.3.3 The proposed hybrid-ε-greedy algorithmThere are several strategies which provide an approximate solution to the bandit prob-lem. Here, we focus on two of them: the greedy strategy, which always chooses thebest documents, thus uses only exploitation; the ε-greedy strategy, which adds somegreedy exploration policy, choosing the best documents at each step if the policy re-turns the greedy documents (probability = ε) or a random documents otherwise (prob-ability = 1 – ε). We propose a two-fold improvement on the performance of the ε-greedy algo-rithm: integrating case base reasoning (CBR) and content based filtering (CBF). Thisnew proposed algorithm is called hybrid-ε-greedy and is described in (Alg. 3). To improve exploitation of the ε-greedy algorithm, we propose to integrate CBRinto each iteration: before choosing the document, the algorithm computes the simi-larity between the present situation and each one in the situation base; if there is a

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situation that can be re-used, the algorithm retrieves it, and then applies an explora-tion/exploitation strategy. In this situation-aware computing approach, the premise part of a case is a specificsituation S of a mobile user when he navigates on his mobile device, while the valuepart of a case is the user’s preferences UP to be used for the recommendation. Eachcase from the case base is denoted as C= (S, UP). Let Sc=(Xlc, Xtc, Xsc) be the current situation of the user, UP c the current user’spreferences and PS={S1,....,Sn} the set of past situations. The proposed hybrid-ε-greedy algorithm involves the following four methods.RetrieveCase() (Alg. 3) Given the current situation Sc, the RetrieveCase method determines the expecteduser preferences by comparing Sc with the situations in past cases in order to choosethe most similar one Ss. The method returns, then, the corresponding case (S s, UPs). Ss is selected from PS by computing the following expression as it done in [4]:   S s = arg max   α j  sim j X c ,X ij   j  (1) S i PS  j  In equation 1, simj is the similarity metric related to dimension j between two situa-tion vectors and αj the weight associated to dimension j. αj is not considered in thescope of this paper, taking a value of 1 for all dimensions. The similarity between two concepts of a dimension j in an ontological semanticdepends on how closely they are related in the corresponding ontology (location, timeor social). We use the same similarity measure as [12] defined by equation 2: sim X c , X i   2  deph( LCS ) (2) (deph( X c )  deph( X ij )) j j j j Here, LCS is the Least Common Subsumer of X jc and Xji, and depth is the numberof nodes in the path from the node to the ontology root.RecommendDocuments() (Alg. 3) In order to insure a better precision of the recommender results, the recommenda-tion takes place only if the following condition is verified: sim(S c, Ss) ≥ B (Alg. 3),where B is a threshold value and sim(S c , S s ) = sim j X c ,X s  j j j In the RecommendDocuments() method, sketched in Algorithm 1, we propose toimprove the ε-greedy strategy by applying CBF in order to have the possibility torecommend, not the best document, but the most similar to it (Alg. 1). We believe thismay improve the user’s satisfaction. The CBF algorithm (Alg. 2) computes the similarity between each documentd=(c1,..,ck) from UP (except already recommended documents D) and the best docu-ment db=(cjb ,.., ckb ) and returns the most similar one. The degree of similarity be-tween d and db is determined by using the cosine measure, as indicated in equation 3:

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Each diary situation entry represents the capture, for a certain user, of contextual in-formation: time, location and social information. For each entry, the captured data arereplaced with more abstracted information using the ontologies. For example the situ-ation 1 becomes as shown in Table 2. Table 2. Semantic diary situation IDS Users Time Place Client 1 Paul Workday Paris Finance client 2 Fabrice Workday Roubaix Social client 3 Jhon Holiday Paris Telecom client From the diary study, we obtained a total of 342 725 entries concerning user navi-gation, expressed with an average of 20.04 entries per situation. Table 3 illustrates anexample of such diary navigation entries. For example, the number of clicks on adocument (Click), the time spent reading a document (Time) or his direct interestexpressed by stars (Interest), where the maximum stars is five. Table 3. Diary navigation entries IdDoc IDS Click Time Interest 1 1 2 2’ ** 2 1 4 3’ *** 3 1 8 5’ *****4.2 Finding the optimal B threshold valueIn order to evaluate the precision of our technique to identify similar situations andparticularly to set out the threshold similarity value, we propose to use a manual clas-sification as a baseline and compare it with the results obtained by our technique. So,we manually group similar situations, and we compare the manual constructed groupswith the results obtained by our similarity algorithm, with different threshold values. Fig. 1. Effect of B threshold value on the similarity accuracy

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Figure 1 shows the effect of varying the threshold situation similarity parameter B inthe interval [0, 3] on the overall precision P. Results show that the best performance isobtained when B has the value 2.4 achieving a precision of 0.849. Consequently, weuse the identified optimal threshold value (B = 2.4) of the situation similarity measurefor testing effectiveness of our MCRS presented below.4.3 Experimental datasetsIn this Section, we evaluate the following algorithms: ε-greedy and hybrid-ε-greedy,described in Section 3.3; CBR-ε-greedy, a version of the hybrid-ε-greedy algorithmwithout executing the CBF. We evaluated these algorithms over a set of similar user situations using the opti-mal threshold value identified above (B = 2.4). The testing step consists of evaluating the algorithms for each testing situation us-ing the traditional precision measure. As usually done for evaluating systems based onmachine learning techniques, we randomly divided the entries set into two subsets.The first one, called “learning subset”, consists of a small fraction of interaction onwhich the bandit algorithm is run to learn/estimate the CTR associated to each docu-ment. The other one, called “deployment subset”, is the one used by the system togreedily recommend documents using CTR estimates obtained from the learning sub-set. Precision Precision Fig. 2. ε Variation on learning subset Fig. 3. ε variation on deployment subset4.4 Results for ε variationEach of the competing algorithms requires a single parameter ε. Figures 2 and 3 showhow the precision varies for each algorithm with the respective parameters. All theresults are obtained by a single run. As seen from these figures, when the parameter ε is too small, there is insufficientexploration; consequently the algorithms failed to identify relevant documents, andhad a smaller number of clicks. Moreover, when the parameter is too large, the algo-rithms seemed to over-explore and thus wasted some of the opportunities to increasethe number of clicks. Based on these results, we choose appropriate parameters foreach algorithm and run them once on the evaluation data.

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We can conclude from the plots that CBR information is indeed helpful for findinga better match between user interest and document content. The CBF also helps hy-brid-ε-greedy in the learning subset by selecting more attractive documents to rec-ommend. Precision Precision Fig. 4. Learning data size Fig. 5. Deployment data size4.5 Valuate sparse dataTo compare the algorithms when data is sparse in our experiments, we reduced datasizes of 30%, 20%, 10%, 5%, and 1%, respectively. To better visualize the comparison results, figures 4 and 5 show algorithms’ preci-sion graphs with the previous referred data sparseness levels. Our first conclusion isthat, at all data sparseness levels, the three algorithms are useful. A second interestingconclusion is that hybrid-ε-greedy’s methods outperform the ε-greedy’s one in learn-ing and deployment subsets. The advantage of hybrid-ε-greedy over ε-greedy is evenmore apparent when data size is smaller. At the level of 1% for instance, we observean improvement of 0.189 in hybrid-ε-greedy’s precision using the deployment subset(0.363) over the ε-greedy’s one (0.174).5 ConclusionThis paper describes our approach for implementing a MCRS. Our contribution is tomake a deal between exploration and exploitation for learning and maintaining user’sinterests based on his/her navigation history. We have presented an evaluation protocol based on real mobile navigation. Weevaluated our approach according to the proposed evaluation protocol. This studyyields to the conclusion that considering the situation in the exploration/exploitationstrategy significantly increases the performance of the recommender system followingthe user interests. In the future, we plan to compute the weights of each context dimension and con-sider them on the detection of user’s situation, and then we plan to extend our situa-tion with more context dimension. Regarding the bandit algorithms we plan to inves-